Large-scale image retrieval has a great potential in commercial, industrial and research applications. Based on web searching techniques, example large-scale image retrieval can implement the use of images based on “bag-of-features” or BOF models. Furthermore, such models can include index histograms that represent features of the images. The BOF models and index histograms can be represented by well known inverted file indexing. For example, an inverted file can be an index data structure storing a mapping of image content, where the mapping can include words or numbers associated with the image content.
For large-scale image retrieval, it is desirable to accurately retrieve similar images that are different in scale or size. Although techniques using BOF models and index histograms have shown to be simple and efficient, such techniques can suffer in terms of accuracy and scalability. To improve retrieval accuracy, various approaches have been proposed, such as large vocabularies, soft quantization, and query expansion. A limitation of such approaches is that they typically ignore spatial information of local features, which has been observed to improve retrieval accuracy.
Although certain large-scale image retrieval models have attempted to make use of spatial information (e.g., translation, rotation, or scaling of images) to improve image retrieval accuracy, such models have certain drawbacks. For example, models employing ranking and re-ranking of image search results can provide random access to raw features of the images, but increase memory resources, and slow down search and retrieval.